Topology optimization of self-supporting support structures for additive manufacturing

In this paper, we formulate the generation of support structures for additive manufacturing as a topology optimization problem. Compared with usual geometric considerations based support structure design, this formulation affords mechanistic meaning to the computed support structures. Moreover, our study reveals that the topology optimization formulation generally leads to self-supporting designs without extraneous self-supporting constraints. We show the generality of the procedure by computing supports for a variety of parts in both two and three dimensions, including a complex model of the mascot of the University of Wisconsin-Madison. The resulting support structures have been 3D printed, demonstrating that the computed designs can successfully be used as supports.